Sentimental impact of fake news on social media using an integrated ensemble framework

Social media has transformed the way we interact with each other in the twenty-first century. As it continues to evolve and shape our lives, it is important to consider both the benefits and the potential risks of these platforms. One of these risks is the spread of misinformation over global platforms like Twitter. The rapid dissemination of false information can have far-reaching consequences, influencing public opinion and potentially leading to real-world harm. One critical area of concern is the impact of fake news on mental health and well-being. This study aims to explore the emotional responses elicited by fake news and provide insights into the psychological effects of false information circulated through social media. To achieve this, a novel Ensemble Hard Voting Classifier (EHVC), which accurately classifies tweets as either fake or real with an accuracy of 93.84% has been developed. Additionally, sentiment analysis has been conducted, yielding an accuracy of 83.02%. These two stand-alone models are then integrated into a unified architecture that analyzes the sentiments triggered by fake news in real-time using the Twitter API. Additionally, this research validates the significance of the relationship between fake news and sentiments in order to substantiate that degrading mental health is a direct consequence of fake news by employing the Pearson’s Chi-Squared test for independence. The proposed methodology results have also been compared and analyzed against conventional machine learning algorithms to demonstrate the efficacy of the EHVC model. This research not only highlights the psychological impacts of fake news but also underscores the importance of addressing misinformation on social media platforms.